Instructions to use hf-internal-testing/tiny-random-Data2VecAudioForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Data2VecAudioForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="hf-internal-testing/tiny-random-Data2VecAudioForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForAudioClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-Data2VecAudioForSequenceClassification") model = AutoModelForAudioClassification.from_pretrained("hf-internal-testing/tiny-random-Data2VecAudioForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e049fe5c3f101286b9d3e8d01681e975a970943bc5a156d2ce5684b665c9c51e
- Size of remote file:
- 314 kB
- SHA256:
- f4623a3507f29376be805ed1d7161e35e59d70cc5c5f69a3ea456b627e923d5c
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